Typically, an average commercial aircraft generates a massive amount of data in each flight. Each part of an aircraft may be fitted with different sensor devices which are capable of monitoring one or more key operating characteristics, such as temperatures, pressures, speeds, flows and/or vibration levels at various places in the aircraft. Data from the aircraft may also be captured to provide context for monitoring systems, including altitude, speed, air temperature, cabin air quantity, and electrical power.
This massive amount of flight data is also known as big data which is a phrase used for sets of data so large or complex that traditional data processing applications are inadequate. Existing challenges with big data include problems rooted in computer technology with analysis, capture, data curation, search, sharing, storage, transfer, visualization, and information privacy.
Because of these existing computer technology complexities of how to effectively analyze this massive amount of aircraft data, prior computer analytic systems for detecting any issues with aircraft primarily have been focused on analyzing smaller directed subsets of this captured flight data. Unfortunately, by initially focusing on smaller subsets of data, rather than the massive set of captured flight data, the resulting analyses have been less accurate and effective in identifying aircraft anomalies.